U.S. patent application number 14/691958 was filed with the patent office on 2015-08-13 for medical information analysis apparatus and medical information analysis method.
The applicant listed for this patent is FUJITSU LIMITED. Invention is credited to TATSUO HAYAKAWA.
Application Number | 20150227714 14/691958 |
Document ID | / |
Family ID | 50730719 |
Filed Date | 2015-08-13 |
United States Patent
Application |
20150227714 |
Kind Code |
A1 |
HAYAKAWA; TATSUO |
August 13, 2015 |
MEDICAL INFORMATION ANALYSIS APPARATUS AND MEDICAL INFORMATION
ANALYSIS METHOD
Abstract
A medical information analysis apparatus includes a specifying
unit and an extraction unit. The specifying unit specifies a second
patient whose medical record is similar to that of a first patient
with reference to the medical records of a plurality of patients.
The extraction unit extracts information from the medical record of
the second patient.
Inventors: |
HAYAKAWA; TATSUO; (Toyoake,
JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FUJITSU LIMITED |
Kawasaki-shi |
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JP |
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|
Family ID: |
50730719 |
Appl. No.: |
14/691958 |
Filed: |
April 21, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/JP2012/079498 |
Nov 14, 2012 |
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14691958 |
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Current U.S.
Class: |
705/3 |
Current CPC
Class: |
G16H 50/70 20180101;
G16H 10/60 20180101 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A non-transitory computer-readable storage medium storing a
computer program that causes a computer to perform a process
comprising: specifying a second patient whose medical record is
similar to a medical record of a first patient with reference to
medical records of a plurality of patients; and extracting
information from the medical record of the second patient.
2. The non-transitory computer-readable storage medium according to
claim 1, wherein the specifying a second patient includes
calculating a degree of similarity between each of a plurality of
record notes included in the medial record of the first patient and
each of a plurality of record notes included in medical records of
other patients, and calculating a total degree of similarity
between record notes for each of the other patients.
3. The non-transitory computer-readable storage medium according to
claim 2, wherein the specifying a second patient includes
extracting a predetermined number of record notes of the other
patients in descending order of the degree of similarity, with
respect to each of the plurality of record notes of the medical
record of the first patient, and calculating the total degree of
similarity of the extracted record notes for each of the other
patients.
4. The non-transitory computer-readable storage medium according to
claim 2, wherein the calculating a degree of similarity includes
performing weighting based on types of record notes to multiply a
result of similarity calculation based on contents of two record
notes, which are objects for calculating the degree of similarity,
by weights corresponding to the two record notes, and taking a
result of multiplication as the degree of similarity between the
two record notes.
5. The non-transitory computer-readable storage medium according to
claim 1, wherein the medical records of the plurality of patients
include physical information indicating physical features of the
patients.
6. The non-transitory computer-readable storage medium according to
claim 1, wherein the specifying a second patient includes
calculating a degree of similarity between the medical record of
the first patient and a medical record of each of other patients,
and taking a predetermined number of other patients in descending
order of the degree of similarity as the second patient provided in
plurality.
7. The non-transitory computer-readable storage medium according to
claim 1, wherein the extracting information includes extracting a
term which does not occur in the medical record of the first
patient from terms occurring in the medical record of the second
patient.
8. A medical information analysis apparatus comprising: a processor
that performs a process including: specifying a second patient
whose medical record is similar to a medical record of a first
patient with reference to medical records of a plurality of
patients; and extracting information from the medical record of the
second patient.
9. A medical information analysis method comprising: specifying, by
a processor, a second patient whose medical record is similar to a
medical record of a first patient with reference to medical records
of a plurality of patients; and extracting, by the processor,
information from the medical record of the second patient.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation application of
International Application PCT/JP2012/079498 filed on Nov. 14, 2012
which designated the U.S., the entire contents of which are
incorporated herein by reference.
FIELD
[0002] The embodiments discussed herein relate to a medical
information analysis program, medical information analysis
apparatus, and medical information analysis method for analyzing
medical information.
BACKGROUND
[0003] With advances in information technology, the range of
computer use has expanded. As a result, computers store an enormous
amount of electronic data. A lot of the electronic data includes
character information. Therefore, some techniques have been
considered to efficiently use such character information included
in electronic data. For example, for Q&A service on the
Internet, there is a technique for providing users, if they have
posted any questions or answers even without clearly representing
or answering about their interests, with recommended questions
according to their knowledge and interests to motivate them to
answer. In addition, there is a method for collecting, managing,
searching, and sharing personal information manually entered as
comments.
[0004] Data management using computers has become widespread also
in the healthcare industry. For example, in the healthcare
industry, electronic medical charts are increasingly adopted mainly
by large-scale hospitals in order to manage diagnosis results and
other reports for each patient. In the field of healthcare, it has
been considered to efficiently use accumulated data on a large
number of patients for medical care. For example, in systems for
searching for similar images on the basis of text added to images,
there is a system for finding desired images promptly and easily.
This system makes it possible to automatically extract, with
respect to an image, keywords or feature values that a searcher
might overlook or might not be aware of and also to find related
cases that a doctor might overlook. In addition, there is a medical
diagnosis report system for preventing radiologists from
overlooking observation points and thereby for providing
high-quality radiological reports. This medical diagnosis report
system extracts terms for specifying radiologic interpretation
points on the basis of observations written on medical diagnosis
reports as check items for radiologic interpretation, obtains
sentences including the extracted radiologic interpretation check
items from observations written on a medical diagnosis report, and
displays the sentences for each of the radiologic interpretation
check items.
[0005] Please see Japanese Laid-open Patent Publications Nos.
2011-186854, 2005-85285, 2004-157623, and 2011-100254.
[0006] Various methods have been proposed for use of medical
information. However, conventional techniques are not yet
sufficient to make good use of a massive amount of data on medical
care. For example, for the medical care of a patient, the medical
records (charts) of other patients suffering from the same symptoms
as the patient under medical care are considered useful. However,
in the current situation, it is not possible to appropriately
determine which of a great number of medical records contains
information that is worth considering for the medical care of a
patient. That is to say, it is not possible to extract information
useful for the patient from the great number of accumulated medical
records.
SUMMARY
[0007] According to one aspect, there is provided a non-transitory
computer-readable storage medium storing a computer program that
causes a computer to perform a process including: specifying a
second patient whose medical record is similar to a medical record
of a first patient with reference to medical records of a plurality
of patients; and extracting information from the medical record of
the second patient.
[0008] The object and advantages of the invention will be realized
and attained by means of the elements and combinations particularly
pointed out in the claims.
[0009] It is to be understood that both the foregoing general
description and the following detailed description are exemplary
and explanatory and are not restrictive of the invention.
BRIEF DESCRIPTION OF DRAWING
[0010] FIG. 1 illustrates an example of a functional configuration
of a medical information analysis apparatus according to a first
embodiment;
[0011] FIG. 2 illustrates an example of a system configuration
according to a second embodiment;
[0012] FIG. 3 illustrates an example of a hardware configuration of
a server that is employed in the embodiment;
[0013] FIG. 4 is a block diagram illustrating a recommendation
function of the server;
[0014] FIG. 5 illustrates an example of a data structure of an
electronic medical chart storage unit;
[0015] FIG. 6 illustrates an example of a data structure of a
registration exclusion dictionary storage unit;
[0016] FIG. 7 is a flowchart illustrating an example of how to
perform a recommendation process;
[0017] FIG. 8 is a flowchart illustrating an example of how to
perform a target-patient record analysis process;
[0018] FIG. 9 illustrates an example of a data structure of a
target-patient-record-based term storage unit;
[0019] FIG. 10 is a flowchart illustrating an example of how to
perform an other-patient record analysis process;
[0020] FIG. 11 illustrates an example of a data structure of a
similar-record-based term storage unit;
[0021] FIG. 12 illustrates similarity relationships between record
notes;
[0022] FIG. 13 is a flowchart illustrating how to perform a similar
patient determination process;
[0023] FIG. 14 illustrates an example of a data structure of a
similar patient information storage unit;
[0024] FIG. 15 illustrates an example of a data structure of a
similar-patient-record-based term storage unit;
[0025] FIG. 16 is a flowchart illustrating how to perform a
reference term extraction process; and
[0026] FIG. 17 illustrates an example of recommendation.
DESCRIPTION OF EMBODIMENTS
[0027] Hereinafter, embodiments will be described with reference to
the accompanying drawings. Features of the embodiments may be
combined unless they exclude each other.
First Embodiment
[0028] FIG. 1 illustrates an example of a functional configuration
of a medical information analysis apparatus according to a first
embodiment. A medical information analysis apparatus S includes a
storage unit 1, a specifying unit 2, and an extraction unit 4. For
example, the medical information analysis apparatus S is a computer
that runs a medical information analysis program to analyze medical
information.
[0029] The storage unit 1 stores the medical records (charts) 1a,
1b, 1c, . . . of a plurality of patients. These medical records 1a,
1b, and 1c, . . . are created for the individual patients. The
medical records 1a, 1b, and 1c, . . . include record notes
indicating the details of the medical care and nursing care for the
patients.
[0030] The specifying unit 2 specifies a second patient whose
medical record is similar to that of a first patient with reference
to the medical records of the plurality of patients. For example,
the specifying unit 2 calculates the degree of similarity between
each of the plurality of record notes 1a-1, 1a-2, 1a-3, . . .
included in the medical record 1a of the first patient and each of
the plurality of record notes 1b-1, 1b-2, 1b-3, . . . included in
the medical records 1b, 1c, . . . of the other patients. The
specifying unit 2 then calculates the total degree of similarity
between record notes for each of the other patients. A total degree
of similarity represents how similar one of the other patients is
to the first patient. Alternatively, the specifying unit 2 may be
designed to extract a predetermined number of record notes of the
other patients in descending order of the degree of similarity,
with respect to each of the plurality of record notes 1a-1, 1a-2,
1a-3, . . . included in the medical record 1a of the first patient,
and calculate the total degree of similarity of the extracted
record notes for each of the other patients.
[0031] The extraction unit 4 extracts information from the medical
record 1b of the second patient. For example, the extraction unit 4
extracts terms that do not occur in the medical record 1a of the
first patient from the terms occurring in the medical record 1b of
the second patient.
[0032] In the above-described medical information analysis
apparatus S, analysis of medical information is started in response
to an analysis instruction specifying a first patient from, for
example, a user. In the analysis of medical information, the
specifying unit 2 first calculates the degree of similarity between
the medical record 1a of the first patient and each of the medical
records 1b, 1c, . . . of the other patients. For example, the
specifying unit 2 calculates the degree of similarity between each
of the record notes 1a-1, 1a-2, 1a-3, . . . included in the medical
record 1a of the first patient and each of the plurality of record
notes 1b-1, 1b-2, 1b-3, . . . included in the medical records 1b,
1c, . . . of the other patients. The specifying unit 2 then
calculates the total degree of similarity between record notes for
each of the other patients. Then, the specifying unit 2 specifies
one or more other patients, in descending order of the total degree
of similarity, as second patients. When specifying the second
patients, the specifying unit 2 sends, for example, information
(similar patient information 3) indicating the specified patients
to the extraction unit 4. Referring to the example of FIG. 1, a
patient "B" is specified as a second patient.
[0033] Then, the extraction unit 4 extracts terms that are
considered useful for the medical care of the first patient, from
the medical record 1b of the second patient. For example, the
extraction unit 4 extracts terms that are not included in the terms
(term array 5) occurring in the medical record 1a of the first
patient from the terms (term array 6) occurring in the medical
record 1b of the second patient. The extraction unit 4 then outputs
the extracted terms, for example, as terms (reference terms 7) that
are useful for the medical care of the first patient 1.
[0034] As described above, information that is useful for the
medical care of the first patient is extracted from the medical
record of the second patient which has similar medical data to that
of the first patient, thereby promoting effective use of medical
information. This results in improving the quality of medical
care.
[0035] In this connection, the number of second patients specified
by the specifying unit 2 is not limited to one. For example, the
specifying unit 2 may be designed to calculate the degree of
similarity between the medical record of the first patient and each
of the medical records of the other patients, and specify a
predetermined number of other patients in descending order of the
degree of similarity as second patients.
[0036] In addition, in calculating the degree of similarity between
record notes, the specifying unit 2 may perform weighting based on
the types of the record notes. For example, the specifying unit 2
multiplies a result of similarity calculation based on the contents
of two record notes, which are objects for calculating the degree
of similarity, by the weights corresponding to the two record
notes, and takes the multiplication result as the degree of
similarity between the two record notes. This makes it possible to
give importance to record notes according to their types and
extract appropriate terms. For example, a weight for medical data
recorded by doctors is set higher than that for medical data
recorded by nurses, so as to preferentially specify other patients
who have similar details of medical care, rather than the details
of nursing care, as second patients.
[0037] In addition, physical information indicating physical
features of patients may be included in the medical records of the
patients. By determining the degree of similarity between medical
records using such physical information, other patients who have
similar physical information to the first patient are specified as
second patients. This makes it possible to extract terms that are
useful for treating a disease whose treatment is greatly different
depending on differences in the physical information of
patients.
[0038] In this connection, the specifying unit 2 and extraction
unit 4 are implemented by, for example, using a processor provided
in the medical information analysis apparatus S. In addition, the
storage unit 1 may be implemented by, for example, using a RAM
(Random Access Memory) provided in the medical information analysis
apparatus S.
Second Embodiment
[0039] The following describes a second embodiment. The second
embodiment relates to an electronic medical chart management system
which allows a plurality of doctors to share knowledge recorded in
electronic medical charts over a network within a hospital.
[0040] FIG. 2 illustrates an example of a system configuration
according to the second embodiment. In an electronic medical chart
management system, a server 100 and a plurality of terminal devices
21 to 23 are connected over a network 10. The terminal devices 21
to 23 are used by doctors 31 and 32 and a nurse 33 to enter
information about the medical care of patients. The information
entered in the terminal devices 21 to 23 are sent to the server 100
over the network 10 and are recorded in the server 100. A
collection of record notes for each patient is treated as an
electronic medical chart for the patient. Each record note is
information that is recorded each time a treatment or nursing care
is provided.
[0041] The server 100 is a computer that manages electronic medical
charts. The server 100 analyzes accumulated electronic medical
charts in response to a recommendation request from a terminal
device 21 to 23, and outputs a recommendation result. The
recommendation request specifies a patient (target patient) for
recommendation. For example, the server 100 extracts keywords that
are not recorded in the electronic medical chart of the target
patient but are recorded in the electronic medical charts of other
patients (similar patients) whose symptoms or treatment histories
are similar to those of the target patient, and outputs the
keywords as a recommendation result.
[0042] FIG. 3 illustrates an example of a hardware configuration of
a server that is employed in the embodiment. The server 100 is
entirely controlled by a processor 101. A RAM 102 and a plurality
of peripherals are connected to the processor 101 via a bus 109.
The processor 101 may be a multiprocessor. The processor 101 may
be, for example, a CPU (Central Processing Unit), a MPU (Micro
Processing Unit), or a DSP (Digital Signal Processor). Some or all
of the functions of the processor 101 may be implemented by using
an ASIC (Application Specific Integrated Circuit), a PLD
(Programmable Logic Device), or other electronic circuits.
[0043] The RAM 102 is used as a primary storage device of the
server 100. The RAM 102 temporarily stores at least part of OS
(Operating System) programs and application programs to be executed
by the processor 101. The RAM 102 also stores various data to be
used while the processor 101 operates.
[0044] The peripherals connected to the bus 109 include an HDD
(Hard Disk Drive) 103, a graphics processing device 104, an input
device interface 105, an optical drive device 106, a device
connection interface 107, and a network interface 108.
[0045] The HDD 103 magnetically writes and reads data on a built-in
disk. The HDD 103 is used as a secondary storage device of the
server 100. The HDD 103 stores the OS programs, application
programs, and various data. As a secondary storage device, a
semiconductor storage device, such as a flash memory, may be
used.
[0046] A monitor 11 is connected to the graphics processing device
104. The graphics processing device 104 displays images on the
screen of the monitor 11 in accordance with instructions from the
processor 101. As the monitor 11, a display device using CRT
(Cathode Ray Tube), a liquid crystal display device, or the like
may be used.
[0047] A keyboard 12 and a mouse 13 are connected to the input
device interface 105. The input device interface 105 gives the
processor 101 signals received from the keyboard 12 and mouse 13.
The mouse 13 is one example of pointing devices, and another
pointing device may be used. Other pointing devices include, for
example, a touch panel, a tablet, a touchpad, a track ball, and so
on.
[0048] The optical drive device 106 reads data from an optical disc
14 with laser light or the like. The optical disc 14 is a portable
recording medium on which data is recorded so as to be read with
reflection of light. As the optical disc 14, a DVD (Digital
Versatile Disc), DVD-RAM, CD-ROM (Compact Disc Read Only Memory),
CD-R (Readable), CD-RW (ReWritable), or the like may be used.
[0049] The device connection interface 107 is a communication
interface that allows peripherals to be connected to the server
100. For example, a memory device 15 and a memory reader-writer 16
may be connected to the device connection interface 107. The memory
device 15 is a recording medium provided with a function for
communication with the device connection interface 107. The memory
reader-writer 16 is a device that performs data read and write on a
memory card 17. The memory card 17 is a card-type recording
medium.
[0050] The network interface 108 is connected to the network 10.
The network interface 108 performs data communication with other
computers or communication devices over the network 10.
[0051] With the above hardware configuration, the processing
functions of the second embodiment may be implemented. In this
connection, the medical information analysis apparatus of the first
embodiment may be configured with the same hardware as the server
100 of FIG. 3.
[0052] The server 100 performs the processing functions of the
second embodiment by, for example, executing a program recorded on
a computer-readable recording medium. The program describing the
contents of processing to be performed by the server 100 may be
recorded on a variety of recording media. For example, the program
to be executed by the server 100 may be stored on the HDD 103. The
processor 101 loads at least part of the program from the HDD 103
to the RAM 102 and executes the program. Alternatively, the program
to be executed by the server 100 may be stored on a portable
recording medium, such as the optical disc 14, memory device 15, or
memory card 17. The program stored in the portable recording medium
is installed in the HDD 103, for example, under the control of the
processor 101, and thereby becomes executable. The processor 101
may read and execute the program directly from the portable
recording medium.
[0053] FIG. 4 is a block diagram illustrating a recommendation
function of the server. The server 100 includes, as data storage
units, an electronic medical chart storage unit 110, a registration
exclusion dictionary storage unit 120, a
target-patient-record-based term storage unit 130, a
similar-record-based term storage unit 140, a similar patient
information storage unit 150, and a similar-patient-record-based
term storage unit 160. For example, part of the storage area of the
RAM 102 or HDD 103 of the server 100 is used for these data storage
units. The server 100 also includes, as data processing units, a
term extraction unit 171, a similar patient determination unit 172,
and a reference term extraction unit 173. These processing units
are implemented by, for example, the processor 101 of the server
100 executing a program stored in the RAM 102 or HDD 103.
[0054] The electronic medical chart storage unit 110 stores record
notes entered by the doctors 31 and 32, the nurse 33, and so on.
The registration exclusion dictionary storage unit 120 stores terms
that are to be excluded from being extracted in a process of
extracting terms from electronic medical charts. The
target-patient-record-based term storage unit 130 stores terms
extracted from the record notes of a patient (target patient) who
is a target for recommendation. The similar-record-based term
storage unit 140 stores terms extracted from similar record notes
to the record notes of the target patient. The similar patient
information storage unit 150 stores information on other patients
(similar patients) whose electronic medical charts have similar
contents to that of the target patient. The
similar-patient-record-based term storage unit 160 stores terms
included in the record notes of the similar patients.
[0055] When receiving a recommendation request specifying a target
patient, the term extraction unit 171 obtains the record notes
included in the electronic medical chart of the target patient from
the electronic medical chart storage unit 110. The term extraction
unit 171 then extracts terms from the obtained record notes. For
example, the term extraction unit 171 performs morphological
analysis to obtain a plurality of terms from the record notes.
Then, the term extraction unit 171 eliminates terms included in a
registration exclusion dictionary stored in the registration
exclusion dictionary storage unit 120 from the terms obtained
through the morphological analysis. In this connection, the term
extraction unit 171 may refer to, for example, a medical term
dictionary to eliminate terms that are not included in the medical
term dictionary. The term extraction unit 171 stores the terms
remaining after the elimination of such terms, in the
target-patient-record-based term storage unit 130.
[0056] In addition, the term extraction unit 171 extracts terms
from record notes that are similar to any of the record notes of
the target patient, among the record notes of the other patients
than the target patient. For example, the term extraction unit 171
performs morphological analysis on the record notes of the other
patients to obtain a plurality of terms therefrom. The term
extraction unit 171 then refers to the registration exclusion
dictionary storage unit 120 to eliminate terms included in the
registration exclusion dictionary from the terms obtained through
the morphological analysis. Then, the term extraction unit 171
stores the terms remaining after the elimination of such terms in
the similar-record-based term storage unit 140.
[0057] The similar patient determination unit 172 ranks the other
patients according to the degrees of similarity in record notes
between the target patient and the other patients. The similar
patient determination unit 172 stores information on a
predetermined number of patients (similar patients), in descending
order of the degree of similarity, in the similar patient
information storage unit 150. In addition, the similar patient
determination unit 172 stores the terms extracted from the record
notes of the similar patients in the similar-patient-record-based
term storage unit 160.
[0058] The reference term extraction unit 173 extracts, as terms
that are useful for the medical care of the target patient, the
terms that are not included in the record notes of the target
patient from the terms extracted from the record notes of the
similar patients. The reference term extraction unit 173 then sends
the extracted terms as a recommendation result to the terminal
device having made the recommendation request. The terminal device
displays the recommendation result.
[0059] Lines connecting between units illustrated in FIG. 4
represent part of communication paths, and communication paths
other than the illustrated ones may be configured. Further, the
similar patient determination unit 172 is one example of the
specifying unit 2 of the first embodiment illustrated in FIG. 1.
Still further, the reference term extraction unit 173 is one
example of the extraction unit 4 of the first embodiment
illustrated in FIG. 1.
[0060] Among the data storage units of the server 100, the
electronic medical chart storage unit 110 and registration
exclusion dictionary storage unit 120 have data registered by an
administrator before the recommendation process is performed.
[0061] FIG. 5 illustrates an example of a data structure of an
electronic medical chart storage unit. The electronic medical chart
storage unit 110 stores electronic medical charts 111, 112, 113, .
. . for individual patients. The electronic medical chart 111
includes record notes 111a, 111b, 111c, 111d, . . . Similarly to
the electronic medical chart 111, the other electronic medical
charts 112, 113, . . . include record notes.
[0062] Each of the record notes 111a, 111b, 111c, 111d, . . .
includes patient ID, record number, type, record date, and record
details. A patient ID is an identifier identifying a patient. A
record number is an identifier identifying a record note within the
server 100. A type indicates the type of a record note. For
example, there are "physical record," "doctor's record", and
"nurse's record" types. In this connection, a weight may be defined
for each type of record notes. For example, a weight for doctor's
records is set higher than that for nurse's records. For example, a
result of similarity calculation between record notes is multiplied
by their corresponding weight values. This evaluates a degree of
similarity between doctor's records higher than that between
nurse's records. A record date indicates when a record note was
recorded.
[0063] In this connection, a weight for each type may previously be
defined by a system administrator, for example. Alternatively, a
user may define desired weights by specifying a weight for each
type in a recommendation request.
[0064] The record note 111a of the "physical record" type includes
the name and physical information of a patient. The physical
information indicates physical features including sex, age, weight,
height and so on. With regard to information which may vary with
time, such as age, weight, or height, the latest information is
set.
[0065] The record note 111b of the "doctor's record" type indicates
the details of doctor's medical care. For example, the record note
111b indicates patient's symptoms, the names of prescribed
medicines, and others.
[0066] Although not exemplified in FIG. 5, the "nurse's record"
type of record notes indicate patients' conditions noticed during
nursing care, for example.
[0067] Other types of record notes may include clinical records
indicating the results of clinical care, rehabilitation records
indicating the progresses of rehabilitation, and others, for
example.
[0068] FIG. 6 illustrates an example of a data structure of a
registration exclusion dictionary storage unit. The registration
exclusion dictionary storage unit 120 stores a registration
exclusion dictionary 121. The registration exclusion dictionary 121
contains terms that are to be excluded from being extracted from
record notes. For example, terms, such as "is" or "are", are
registered in the registration exclusion dictionary 121.
[0069] The server 100 performs the recommendation process using the
electronic medical charts 111, 112, 113, . . . and registration
exclusion dictionary 121 illustrated in FIGS. 5 and 6.
[0070] FIG. 7 is a flowchart illustrating an example of how to
perform a recommendation process. In this connection, the
recommendation process is performed when a recommendation request
specifying a target patient is made from any terminal device.
[0071] (Step S101) The term extraction unit 171 performs a record
analysis process for the target patient. Through this process, the
terms extracted from the record notes included in the electronic
medical chart of the target patient are stored in the
target-patient-record-based term storage unit 130. This
target-patient record analysis process will be described in detail
later (see FIG. 8).
[0072] (Step S102) The term extraction unit 171 performs a record
analysis process for the patients (other patients) other than the
target patient. Through this process, the terms extracted from the
record notes having similar contents to those of the electronic
medical chart of the target patient among the record notes included
in the electronic medical charts of the other patients are stored
in the similar-record-based term storage unit 140. This
other-patient record analysis process will be described in detail
later (see FIG. 10).
[0073] (Step S103) The similar patient determination unit 172
performs a similar patient determination process to find other
patients (similar patients) similar to the target patient. Through
this process, the patient IDs of a predetermined number of patients
with high degrees of similarity are stored in the similar patient
information storage unit 150. In addition, the terms extracted from
the record notes of the electronic medical charts of the similar
patients are stored in the similar-patient-record-based term
storage unit 160. This similar patient determination process will
be described in detail later (see FIG. 13).
[0074] (Step S104) The reference term extraction unit 173 performs
a reference term extraction process to extract terms (reference
terms) that are useful for the medical care of the target patient.
The extracted reference terms are sent to the terminal device
having made the recommendation request. Then, the reference terms
are displayed on the monitor of the terminal device. This reference
term extraction process will be described in detail later (see FIG.
16).
[0075] The recommendation is performed in the way as described
above. The following describes in detail each step of FIG. 7.
[0076] FIG. 8 is a flowchart illustrating an example of how to
perform a target-patient record analysis process. The
target-patient record analysis process is performed when a
recommendation request is made.
[0077] (Step S111) The term extraction unit 171 obtains one
unselected record note included in the electronic medical chart of
the target patient from the electronic medical chart storage unit
110. In this connection, the term extraction unit 171 may
previously filter the record notes of the electronic medical chart
of the target patient with SQL (Structured Query Language). In this
case, the term extraction unit 171 sequentially obtains the record
notes remaining after the filtering.
[0078] (Step S112) The term extraction unit 171 determines whether
any record note was obtained at step S111. If the electronic
medical chart of the target patient includes one or more record
notes that have not been obtained, it means that a record note is
obtained at step S111. If all of the record notes in the electronic
medical chart of the target patient have been obtained already, it
means that no record note is obtained at step S111. When any record
note was obtained, the process proceeds to step S113. When no
record note was obtained, then the target-patient record analysis
process is completed.
[0079] (Step S113) The term extraction unit 171 performs
morphological analysis on the obtained record note, starting with
its beginning. For example, the term extraction unit 171 performs
the morphological analysis on each character string describing the
contents of the record note, in order to extract and obtain a term
from the character string, in order starting with the first one. In
this connection, before performing the morphological analysis, the
term extraction unit 171 may convert characters that are
representable in both one byte and two bytes into characters in
either one byte or two bytes in the record note. For example,
before performing the morphological analysis, the term extraction
unit 171 converts one-byte katakana into two-byte katakana, or
two-byte alphabetical characters into one-byte alphabetical
characters.
[0080] In addition, the term extraction unit 171 may eliminate
two-byte numbers, one-byte numbers, and symbols from the record
note. This is because such numbers and symbols are likely to cause
errors in similarity calculation. However, many numbers and symbols
included in the "physical record" type of record notes are
meaningful. Therefore, for example, the term extraction unit 171 is
designed not to eliminate numbers and symbols from the "physical
record" type of record notes.
[0081] (Step S114) The term extraction unit 171 determines whether
a new term was obtained through the morphological analysis. For
example, in the case where the morphological analysis is done up to
the last of the record note, and a process including lexical
category check of all terms and so on is complete, no new term is
obtained. If there is one or more terms that have not undergone the
lexical category check or the like, a term may be obtained. If any
term was obtained, the process proceeds to step S115. If no term
was obtained, the process proceeds to step S120.
[0082] (Step S115) Since the term was obtained, the term extraction
unit 171 checks the lexical category of the obtained term.
[0083] (Step S116) The term extraction unit 171 determines whether
the lexical category of the obtained term needs to be registered.
For example, lexical categories that need to be registered are
defined in advance in the term extraction unit 171. The lexical
categories to be registered include "noun," "verb," "adjective,"
and "adjectival verb," for example. "Particle" and "adverb" are
likely to cause errors in similarity calculation, and therefore are
excluded from registration. If the lexical category of the obtained
term needs to be registered, the process proceeds to step S117. If
the lexical category of the obtained term does not need to be
registered, the process proceeds back to step S113.
[0084] (Step S117) The term extraction unit 171 checks whether the
obtained term is included in the registration exclusion dictionary
121 stored in the registration exclusion dictionary storage unit
120. In this connection, in the case where a noun follows another
noun, for example, the term extraction unit 171 recognizes a
combination of these nouns as a different term. For example, in the
case where "tendency" follows "improvement", three terms,
"improvement," "tendency," and "improvement tendency", are taken as
terms to be checked. In addition, in the case where a term includes
four or more characters and the term ends with "--" (prolonged
sound symbol), the term extraction unit 171 may delete "--." For
example, this symbol is found when terms like "server" and "user"
are written in Japanese.
[0085] (Step S118) The term extraction unit 171 determines whether
to exclude the obtained term from registration. For example, if the
obtained term is included in the registration exclusion dictionary
121, the term extraction unit 171 determines to exclude the term
from registration. If the term is to be excluded from registration,
the process proceeds back to step S113. If the term is not to be
excluded from registration, the process proceeds to step S119.
[0086] (Step S119) The term extraction unit 171 adds the term
obtained at step S113 to the term array of the record note obtained
at step S111. Then, the process proceeds back to step S113.
[0087] (Step S120) Since no term was obtained at step S113, the
term extraction unit 171 determines that the analysis of the
obtained record note is complete, and then stores the term array of
the record note obtained at step S111 in association with the
record note in the target-patient-record-based term storage unit
130. Then, the process proceeds back to step S111.
[0088] As described above, the term array corresponding to each
record note included in the electronic medical chart of the target
patient is stored in the target-patient-record-based term storage
unit 130.
[0089] FIG. 9 illustrates an example of a data structure of a
target-patient-record-based term storage unit. A term array table
131 is stored in the target-patient-record-based term storage unit
130. The term array table 131 is a data table that contains terms
extracted from the record notes of the target patient, for each of
the record notes. The term array table 131 includes fields for
patient ID, record number, type, and term array. The patient ID
field contains the patient ID of a target patient. The record
number field contains the record number of a record note included
in the electronic medical chart of the target patient. The type
field indicates the type of the corresponding record note. The term
array field lists the terms extracted from the corresponding record
note.
[0090] The following describes an other-patient record analysis
process.
[0091] FIG. 10 is a flowchart illustrating an example of how to
perform an other-patient record analysis process. The other-patient
record analysis process is performed after the target-patient
record analysis process is completed.
[0092] (Step S131) The term extraction unit 171 obtains one
unselected record note of the electronic medical chart of a patient
(another patient) other than the target patient from the electronic
medical chart storage unit 110. In this connection, the term
extraction unit 171 may previously filter the record notes of the
electronic medical chart of the other patient with SQL.
[0093] (Step S132) The term extraction unit 171 determines whether
any record note was obtained at step S131. If the electronic
medical chart of at least one of the other patients includes one or
more record notes that have not been obtained, it means that a
record note is obtained at step S131. If all of the record notes in
the electronic medical charts of all the other patients, it means
that no record note is obtained at step S131. If a record note was
obtained, the process proceeds to step S133. If no record note was
obtained, the other-patient record analysis process is
completed.
[0094] (Step S133) The term extraction unit 171 performs the
morphological analysis on the obtained record note, starting with
its beginning, in order to obtain a term in order starting with the
first one. This step is executed in the same way as step S113 of
FIG. 8.
[0095] (Step S134) The term extraction unit 171 determines whether
a new term was obtained through the morphological analysis. If a
term was obtained, the process proceeds to step S135. If no term
was obtained, the process proceeds to step S140.
[0096] (Step S135) Since the term was obtained, the term extraction
unit 171 checks the lexical category of the obtained term.
[0097] (Step S136) The term extraction unit 171 determines whether
to register the lexical category of the obtained term. If the
lexical category of the obtained term needs to be registered, the
process proceeds to step S137. If the lexical category of the
obtained term does not need to be registered, the process proceeds
back to step S133.
[0098] (Step S137) The term extraction unit 171 checks whether the
obtained term is included in the registration exclusion dictionary
121 stored in the registration exclusion dictionary storage unit
120. This step is executed in the same way as step S117 of FIG.
8.
[0099] (Step S138) The term extraction unit 171 determines whether
to exclude the obtained term from registration. If the term is to
be excluded from registration, the process proceeds back to step
S133. If the term is not to be excluded from registration, the
process proceeds to step S139.
[0100] (Step S139) The term extraction unit 171 adds the term
obtained at step S133 to the term array of the record note obtained
at step S131. Then, the process proceeds back to step S133.
[0101] (Step S140) Since no term was obtained at step S133, the
term extraction unit 171 determines that the analysis of the
obtained record note is complete, and then calculates the degree of
similarity (0 or greater and 1 or less) between the obtained record
note and each of the plurality of record notes of the target
patient. The term array extracted from each record note is used for
the similarity calculation. For example, the term extraction unit
171 compares the term array generated at step S139 with the term
array of the record note stored in the target-patient-record-based
storage unit 130 to calculate the degree of similarity with TF
(Term Frequency)-IDF (Inverse Document Frequency). TF-IDF is a
technique for calculating the degree of importance of a term in a
document on the basis of the term occurrence frequency (TF) and
inverse document frequency (IDF). Using the TF-IDF decreases the
degree of importance of a term if it is included in many record
notes and increases the degree of importance of a term if it occurs
only in specific record notes.
[0102] For example, the term extraction unit 171 calculates the
degree of similarity between each record note of the target patient
and the obtained record note of the other patient using the degrees
of importance of the terms extracted from the record notes in the
vector space model. In the vector space model, vectors are created
using the degrees of importance of the terms included in two record
notes to be compared with each other, and the cosine of the angle
.theta. (cos .theta.) between the two vectors is taken as the
degree of similarity.
[0103] In addition, the term extraction unit 171 is able to define
a weight for each type of record notes of electronic medical
charts. In this case, a higher weight is defined for records of
higher importance. For example, weights "1.2," "1.1," and "0.9" are
defined for doctor's records, nurse's records, and other records,
respectively. In addition, a weight for physical information may be
set higher than that for the other record notes. A higher weight
for the physical information increases the degrees of similarity of
record notes of other patients who have similar physical features
to the target patient. The term extraction unit 171 multiplies a
degree of similarity calculated in the vector space model by the
weight values corresponding to the types of the compared record
notes. Then, the term extraction unit 171 takes the result of
multiplication by the weights as a final degree of similarity.
[0104] (Step S141) The term extraction unit 171 determines whether
the obtained record note has a high degree of similarity to any
record note of the target patient. For example, the term extraction
unit 171 arranges, with respect to each record note of the target
patient, the record notes of the other patients in the descending
order of the degree of similarity to the record note. If the
obtained record note is one of the top 20 record notes, the term
extraction unit 171 determines that the record note has a high
degree of similarity. If the obtained record note has a high degree
of similarity to any record note of the target patient, the process
proceeds to step S142. If the obtained record note does not have a
high degree of similarity to any record note of the target patient,
the process proceeds back to S131.
[0105] In this connection, there is a case where, with respect to
one record note of the target patient, a plurality of record notes
of another patient falls in a predetermined number of top record
notes in the similarity ranking. In this case, the term extraction
unit 171 determines that the record notes other than the highest
ranking one among the plurality of record notes of the other
patient do not have high degrees of similarity.
[0106] (Step S142) The term extraction unit 171 stores the term
array extracted from the obtained record note in association with
one or a plurality of record notes of the target patient that have
high degrees of similarity to the obtained record note in the
similar-record-based term storage unit 140. In this connection,
there is a case where, because of a new record note stored, the
number of record notes of other patients associated with a record
note of the target patient exceeds a predetermined value (for
example, 20). In this case, the term extraction unit 171 deletes
the term array of the record note with the lowest degree of
similarity among the record notes of the other patients associated
with the record note of the target patient from the
similar-record-based term storage unit 140. Then, the process
proceeds back to step S131.
[0107] As described above, the term arrays extracted from the
record notes of other patients which are similar to each record
note of the target patient are stored in the similar-record-based
term storage unit 140.
[0108] FIG. 11 illustrates an example of a data structure of a
similar-record-based term storage unit. A term array table 141,
142, 143, . . . for each record note of the target patient is
stored in the similar-record-based term storage unit 140. Each term
array table 141, 142, 143, . . . is given the record number of a
corresponding record note of the target patient. In addition, each
term array table 141, 142, 143, . . . includes fields for patient
ID, record number, type, term array, and similarity.
[0109] The patient ID field contains the patient ID of another
patient. The record number field contains the record number of the
record note of the other patient which has similar contents to a
record note of the target patient. The type field indicates the
type of the record note. The term array field contains the term
array extracted from the record note. The similarity field
indicates the degree of similarity to the record note of the target
patient.
[0110] As described above, each term array table 141, 142, 143, . .
. contains the term arrays of record notes of other patients which
are similar to a record note of the target patient in association
with the record note of the target patient. In this connection, the
term arrays of a plurality of record notes of another patient are
not registered in a single term array table. That is to say, the
term array of only one record note per patient is allowed to be
recorded in a single term array table. However, the term array of
one record note of another patient may be registered in a plurality
of term array tables.
[0111] A plurality of record notes of the target patient and the
record notes of other patients which are similar to the record
notes are associated with each other by the term array tables 141,
142, 143, . . . stored in the similar-record-based term storage
unit 140.
[0112] FIG. 12 illustrates similarity relationships between record
notes. A predetermined number of top record notes of other patients
in a similarity ranking are associated with each of the record
notes 41, 42, 43, 44, 45, . . . of the target patient. For example,
a predetermined number of record notes 51, 52, 53, 54, . . . of
other patients which are similar to the record note 41 of the
target patient are associated with the record note 41. A
predetermined number of record notes 61, 62, 63, 64, . . . of other
patients which are similar to the record note 45 of the target
patient are associated with the record note 45. If the target
patient has 1000 record notes of and the top 20 record notes of
other patients in the similarity ranking are associated with each
of the record notes of the target patient, term arrays are
extracted from 20000 record notes of the other patients.
[0113] Patients (similar patients) having similar treatment
histories to the target patient are determined based on the record
notes of the other patients associated with each of the plurality
of record notes of the target patient.
[0114] FIG. 13 is a flowchart illustrating how to perform a similar
patient determination process. This process is performed after the
other-patient record analysis process is completed.
[0115] (Step S151) The similar patient determination unit 172
calculates, for each patient ID of the other patients, the total
degree of similarity of the record notes of the other patient which
are similar to any of the record notes of the target patient. For
example, the similar patient determination unit 172 obtains, for
each patient ID of the other patients, the degrees of similarity
from the records having the patient ID from the
similar-record-based term storage unit 140. Then, the similar
patient determination unit 172 calculates the total degree of
similarity for each patient ID.
[0116] (Step S152) The similar patient determination unit 172
compares the total degrees of similarity with each other, and
determines a predetermined number (for example, 20) of top patients
as similar patients to the target patient. For example, the similar
patient determination unit 172 sorts the patient IDs of the other
patients in descending order of the total degree of similarity. The
similar patient determination unit 172 extracts a predetermined
number of top patient IDs on the basis of the sorting result. Then,
the similar patient determination unit 172 stores the extracted
patient IDs in the similar patient information storage unit
150.
[0117] (Step S153) The similar patient determination unit 172
obtains the term arrays of the record notes of the similar
patients. For example, the similar patient determination unit 172
extracts records with the patient IDs of the similar patients from
the similar-record-based term storage unit 140, and eliminates the
others. The similar patient determination unit 172 then stores the
extracted records in the similar-patient-record-based term storage
unit 160.
[0118] As described above, similar patients are specified. With
this process, for example, "patients who have many high-ranking
record notes in similarity to the record notes of the electronic
medical chart of the target patient" are specified as the similar
patients. In addition, "patients who do not have many record notes
which are similar to record notes of the electronic medical chart
of the target patient but have some record notes that each have
high degrees of similarity" may be specified as the similar
patients.
[0119] A list of the specified similar patients is stored in the
similar patient information storage unit 150.
[0120] FIG. 14 illustrates an example of a data structure of a
similar patient information storage unit. A similar patient list
151 is stored in the similar patient information storage unit 150.
The similar patient list 151 lists top patients in a similarity
ranking.
[0121] Referring to the example of FIG. 14, the similar patient
list 151 indicates the positions of patients in the ranking, the
patient IDs, and the degrees of similarity.
[0122] With regard to the patients registered in the similar
patient list 151, the term arrays extracted from record notes of
the patients are stored in the similar-patient-record-based term
storage unit 160.
[0123] FIG. 15 illustrates an example of a data structure of a
similar-patient-record-based term storage unit. A term array table
161, 162, 163, . . . for each record note of the target patient is
stored in the similar-patient-record-based term storage unit 160.
The term array tables 161, 162, 163, . . . have the same data
structure of the term array tables 141, 142, 143, . . . stored in
the similar-record-based term storage unit 140 illustrated in FIG.
11. However, only records containing the patient IDs of similar
patients are registered in the term array tables 161, 162, 163, . .
. in the similar-patient-record-based term storage unit 160. For
example, a record containing a patient ID "Z" is included in the
term array table 141 in the similar-record-based term storage unit
140 illustrated in FIG. 11. It is now assumed that the patient with
the patient ID "Z" is not a similar patient. In this case, the
record with the patient ID "Z" is not included in the term array
table 161 in the similar-patient-record-based term storage unit 160
illustrated in FIG. 15.
[0124] Terms that are useful for the medical care of the target
patient are extracted from the terms stored in the
similar-patient-record-based term storage unit 160.
[0125] FIG. 16 is a flowchart illustrating how to perform a
reference term extraction process. This process is performed after
the similar patient determination process is completed.
[0126] (Step S161) The reference term extraction unit 173 combines
(merges) the terms extracted from the record notes of the target
patient and the terms extracted from the record notes of the
similar patients.
[0127] (Step S162) The reference term extraction unit 173 gives a
unique number (term ID) to each term after the merging. For
example, the reference term extraction unit 173 uses the position
number of a term in the order of occurrence of terms in the merged
term array as the term ID of the term. At this time, the
associations between terms and record notes are cleared and a list
of occurring terms is generated for each patient.
[0128] (Step S163) The reference term extraction unit 173 extracts
reference terms from the occurring term list of each patient. For
example, the reference term extraction unit 173 selects reference
terms by applying the collaborative filtering (CF) technique. In
the collaborative filtering, for example, users' actions are
recorded, and other users having similar actions to a specified
user are considered as users having similar preferences to the
specified user. Then, it is expected that the specified user likes
similar actions to the other users having the similar preferences,
and actions that were not taken by the specified user but were
taken by the other users having the similar preferences are
recommended to the specified user. By using the "terms" extracted
from record notes in place of the "actions" used in the
collaborative filtering, it is possible to extract terms that have
not been considered for the target patient but are considered
useful for future medical care from the record notes of the other
users who have similar details of medical care to the target
patient.
[0129] Therefore, the reference term extraction unit 173 extracts
terms (in plurality) that are not included in the occurring term
list of the target patient from the occurring term lists of the
other patients determined to have similar preferences with the
collaborative filtering, as reference terms.
[0130] (Step S164) The reference term extraction unit 173 outputs
the extracted reference terms as a recommendation result. For
example, the reference term extraction unit 173 transmits the list
of reference terms to the terminal device having made the
recommendation request.
[0131] As described above, it is possible to extract terms useful
for the medical care of the target patient from the terms included
in the record notes of other patients having similar details of
medical care.
[0132] FIG. 17 illustrates an example of recommendation. Referring
to the example of FIG. 17, the patient with a patient ID "A" is
taken as a target patient. In this case, similar patients are
determined by comparing terms included in the record notes of the
patient with the patient ID "A" and the other patients. Referring
to the example of FIG. 17, the patients with patient IDs "B," "C,"
"D," "E" are determined as similar patients.
[0133] Then, reference terms are extracted by comparing the terms
included in the record notes of the target patient (patient ID "A")
with the terms included in the record notes of the similar patients
(patient IDs "B," "C," "D," and "E"). For example, as tendencies of
the patients with high degrees of similarity to the patient with
the patient ID "A," many terms related to thyroiditis occur, and
"Levothyroxine sodium hydrate" occurs many times as a medical
agent. As a tendency of the patient with patient ID "A," the terms
"myasthenia gravis" and "decline of functions" do not occur.
Therefore, the terms "myasthenia gravis" and "decline of functions"
are extracted as reference terms and are sent as a recommendation
result.
[0134] As described above, it is possible to detect similar
patients corresponding to electronic medical charts whose contents
are similar to those of the electronic medical chart of the target
patient, extract terms (keywords) useful for the medical care of
the target patient from the electronic medical charts of the
similar patients, and propose them, for example, to a target
patient's doctor. As a result, it is possible to prevent
overlooking any important matters to be considered for the symptoms
of the patient when a treatment plan is determined, to thereby
appropriately determine a treatment plan for the target patient and
improve the quality of medical care. In addition, it is possible to
reduce the degree of dependency on the abilities of individual
doctors when treatment plans are determined and to thereby provide
a high and uniform level of medical service.
[0135] In addition, the degree of similarity between each of the
plurality of record notes included in the medical record of a
target patient and each of the plurality of record notes included
in the medical record of each of the other patients is calculated,
the total degree of similarity is calculated for each of the other
patients, and a predetermined number of other patients are taken in
descending order of the total degree of similarity as similar
patients. This enables the target patient's doctor to determine an
appropriate treatment plan with reference to the details of medical
care for a large number of similar patients. In particular, since a
large-scale hospital has a massive number of electronic medical
charts, a treatment plan for the target patient is determined with
reference to the past medical data of various patients, which makes
it possible to provide more appropriate medical care.
[0136] Still further, a predetermined number of record notes of
other patients are extracted in descending order of the degree of
similarity with respect to each of the plurality of record notes
included in the medical record of the target patient, and the total
degree of similarity of the extracted record notes is calculated
for each of the other patients. That is, unuseful record notes are
eliminated during the extraction of record notes. As a result, even
if a patient who suffers from similar symptoms to the target
patient has an electronic medical chart including a large number of
record notes that are not useful for the target patient due to
treatments for different symptoms, the patient who suffers from the
similar symptoms is appropriately detected. For example, consider
the case where the target patient has treatments for diabetes. In
this case, other patients with diabetes are expected to be
extracted as similar patients. Now, also assume that a patient with
diabetes has a long-term dermatology treatment, irrespective of
diabetes. If electronic medical charts as a whole are compared with
each other in this situation, a possibility of the occurrence of
diabetes-related terms regarding the patient with diabetes
decreases, and therefore the patient with diabetes may not be
determined as a similar patient. By contrast, in the second
embodiment, record notes are compared with each other for
similarity relationship and record notes with low degrees of
similarity are eliminated, so that the record notes relating to
treatments other than diabetes treatment are eliminated, and the
patient with diabetes is appropriately determined as a similar
patient. That is, the similar patient determination has an improved
accuracy.
[0137] Still further, weighting based on the types of record notes
may be performed in calculating the degree of similarity between
record notes. For example, a result of similarity calculation based
on the contents of two record notes, which are objects for
calculating the degree of similarity, is multiplied by the weights
corresponding to the two records notes, and the multiplication
result is taken as the degree of similarity between the two record
notes. For example, a weight for record notes indicating doctors'
medical care may be defined higher than that for record notes
indicating nurses' care. This enables a doctor to refer to the
details of other doctors' medical care when determining a treatment
plan. In the case where a nurse makes a recommendation request for
appropriate nursing care for symptoms of a patient, a weight for
record notes indicating nurses' care is defined higher than that
for record notes indicating doctors' medical care, thereby allowing
the nurse to receive an appropriate recommendation result.
[0138] Still further, physical information is included in record
notes. This makes it possible to output, as a recommendation
result, terms indicating treatments that were given to other
patients who were similar in age, sex, weight, height, or the like
to a target patient and have similar details of medical care to the
target patient, and that have not been given to the target
patient.
[0139] Still further, a weight for record notes including physical
information may be defined higher than that for the other types of
record notes. By defining the highest weight for physical
information, it becomes possible to preferentially determine other
patients having similar physical features to the target patient as
similar patients. In the recommendation using such weighting, terms
useful for determining a treatment plan for a disease whose
symptoms and treatment details vary depending on, for example, sex
and age, are obtained as a recommendation result.
[0140] Still further, by extracting the terms which do not occur in
the medical record of the target patient from the terms occurring
in the plurality of record notes included in the medical records of
similar patients, it becomes possible to prevent unuseful terms
from being extracted. As a result, a doctor who receives a
recommendation result is able to promptly determine a treatment
plan with reference to the recommendation result without being
misled by unuseful terms.
[0141] According to one aspect, it becomes possible to extract
information useful for the medical care of a patient from medical
records.
[0142] All examples and conditional language provided herein are
intended for the pedagogical purposes of aiding the reader in
understanding the invention and the concepts contributed by the
inventor to further the art, and are not to be construed as
limitations to such specifically recited examples and conditions,
nor does the organization of such examples in the specification
relate to a showing of the superiority and inferiority of the
invention. Although one or more embodiments of the present
invention have been described in detail, it should be understood
that various changes, substitutions, and alterations could be made
hereto without departing from the spirit and scope of the
invention.
* * * * *